At times, a user will want to search for a product by color even though it is an attribute that cannot be described adequately using words. For example, other than using rudimentary color names, such as “red” and “blue,” searching for products of a particular shade using color as a parameter is extremely difficult, even when the color is relatively popular and intuitively should be easy to locate. For example, there are numerous colors which would fit the simple “red” or “blue” description, and searching using the textual word “red” is not likely to bring up the specific red or the specific product of interest. Also, searches based on a particular type of color by name, such as “rose red” or “ocean blue,” are unlikely to turn up the color of interest, as there may be a number of different colors, each with a different name or with multiple names varying by the naming convention used. Similarly, searching for a pattern made of colors, such as “blue and red stripes” is unlikely to turn up the desired pattern of particular colors.
Many of the drawbacks involving color-based searching stem from the nature of internet searching, which has historically been text-based, thus requiring a user to enter text into a search engine to describe the information sought. With regard to color, textual color names are typically tagged or embedded beneath an image of a product or associated webpage as metadata, making it virtually impossible to obtain reliable and complete search results when specific color shades are sought. More specifically, because many search systems that implement searching based on a color (or a pattern) are operable only as text searching, a system may allow a user to select a color by name or even “click” on the color (in the form of a color swatch) and then search for the selected color. However, in these instances, the system typically converts the inputted search parameter to a text-string associated with or representing a particular color. For example, a search system may search based on clicking red swatch on a webpage but converts the click to a search for “red” as text, but not as an actual color. In such a system, the name of the color “red” is “tagged” to an image by way of a text string and the search is based on matching the input “red” to the text string “red” on the tag, and not to the color. From a consumer's perspective, such a system is insufficient to reliably capture all relevant products that are currently available in the particular shade of red that are being sought. From a Merchant perspective, such a system does not allow for dynamic analysis or codification of color that is a crucial but missing data set in understanding consumer preferences.
Even color systems that offer naming conventions suffer from underlying drawbacks in their inconsistent application by Merchant users and their vendors. For example, a wholesale buyer for a retailer may decide to order a line of products from a vendor in a color that is identified as “cobalt blue.” A second wholesale buyer at the same retailer may order another line of products from a second vendor in a color that the second buyer also identifies “cobalt blue,” having the intention that the colors be precisely the same so that a purchaser of product from the first line will be more inclined to purchase the second line of product as a matching set. Indeed, the variation in color between two products that purportedly have the ‘same color’ can be remarkable when the products are placed side by side. The lack of consistency among vendors and suppliers, even when the same color names are utilized, is often not appreciated until after the products arrive, at which time it is too late to ameliorate the situation.
Direct searching based on a particular color or a swatch has not been effectively accomplished with text-based systems or search systems that lack a universal color system. For example, if a user is in possession of one article of clothing and wishes to purchase a matching item, existing tools leave the user with the burden of determining the color of the clothing and what a matching color might be. Thus, the user is left to matching based on what “appears” to match (subject to variations in color on a screen).
Current systems further lack the ability to aggregate a user's preferred and/or customized colors onto a unified area or palette for purposes of identifying and searching for products. Individuals typically have preferred colors. and it would be beneficial to have that group of preferred colors collected and readily available to that user in a single palette. Also, use of the palette for forming color combinations and to perform searches based on a primary color and a secondary color (and a pattern) are lacking in the prior art. To that end, it would be beneficial to have that group of preferred colors identified, collected and readily available to that user in a single palette for effective color-based searching. Since these searches are presently unavailable, the data associated with these searches is also unavailable to be used for any data analytics, real-time or otherwise. Such data analytics would be useful to merchants and/or manufacturers for both marketing and operations purposes. These analytics could relate to user preferences for colors and patterns by analyzing user browsing and purchasing behavior, and with associated user data, such as demographic data. Such information can be made available across a variety of user variables (e.g., gender, age, geography, etc.).
In addition to the deficiencies and drawbacks of current systems for consumers, there are many related deficiencies for merchants, retailers, wholesalers and/or manufacturers as well. To start, the captured data can be used for targeted or micro-targeted advertising based on, for example, user preferences, preferences of affinity group members, or user purchase/browsing history.
While current inventory management systems (IMS) include inventory reporting and analysis, and current supply chain management (SCM) systems include production reporting, the merchants, retailers, wholesalers and/or manufacturers currently lack real-time consumer related data to properly forecast and respond to color-trending data. Because of its ability to identify histories of user browsing and purchases in combination with user demographics, the claim invention allows Merchants to forecast color trends in real time and determine, by product and demographics, which colors will be most successful, and plan supply chain and inventory management accordingly. Retailers and manufacturers currently rely on focus groups, which can include as many as 30,000 people in the relevant demographics and geo-locations to sample and obtain user color information. Such an effort is both time-consuming and costly. Because gathering and analyzing of data from the group can take up to eighteen months, once a sampling is complete, the data may no longer be relevant. The SCM must be adjusted in real-time to create the appropriate adjustments during the manufacturing process. More and more retailers and wholesalers are working with “just in time” inventory, shifting the onus from the retailer to the vendor or the factory. Adjustments in the SCM are the only solution to the inventory problems. Thus, by capturing user preferences based off of a purchase history, browsing history, word association, and color selections, all captured in real time, a relevant collection of data may be obtained and used by Merchants to enhance the consumer experience and streamline operations.